This disclosure relates generally to detecting anomalies in the measurement and distribution of utilities.
Increasingly, the utilities industry is moving to a model where metering is becoming automated which enables more accurate and timely measurement of consumption of the utilities by end customers. Typically these meters provide measurements of consumption and a wireless communication capability to broadcast utility consumption when polled. Although new solid state devices are more accurate than electric-mechanical meters, there are a variety of new vulnerabilities that impact the reliability of the meter. Further, physical access to the meter is not always possible and it is difficult to validate consumption patterns.
In these cases, advanced analytics can be utilized to compare changes in consumption within the customer and across customers to determine abnormalities in usage. Metering issues can be grouped into three broad categories meter live abuse, meter failure, and meter bypass/fraud. Another category, zero use can be generally mapped directly to meter failure or meter bypass/fraud and is thus assigned a subcategory under these broad assessments.
Meter live abuse is a straight forward situation detected where a meter shows usage when the formal billing relationship with the customer has ceased. A common situational example of this occurrence can be seen when a tenant vacates a property and shortly afterward an electrical draw is seen at the property. When meter live abuse is detected, there is a return on investment calculation needed to decide whether a field representative should physically shut power off to the property, whether they should engage the new tenant and conduct the research necessary to backbill the use or whether it is anticipated that the new tenant will soon contact the utility company to establish a billing relationship. In cases of residential meter live abuse, it is often difficult for a utility to prove the relationship and thus backbilling is rarely seen in practice. In commercial relationships, depending on regulatory issues, backbilling is often done up to a span of multiple years.
Meter failure can constitute meters that no longer measure consumption accurately or have a systematic inaccuracy. Some meter failures are a consequence of the age of the meter. Meter failure rates can be particularly high with electromechanical meters, but even solid state meters can suffer damage that impacts the ability for the meter to correctly measure electricity consumption. The main goal is to determine whether a fluctuation in energy consumption is a normal variation in a properties' usage, related to an explained event such as a period of vacation, or whether there are usage characteristics that point to higher likelihood that the meter is not working properly, such as violating minimum draw calculations in the case of zero use readings or measuring fluctuations that are not consistent with typical variations in the history of measurements associated with the meter.
Meter by-pass/fraud is a more sinister phenomenon where a meter is being bypassed, broken or tampered with to measure a fraction of the consumption or force no reading of electrical consumption. In electrical energy distribution this could involve running jumpers around the meter to bypass the meter for a period of time, or it could involve removal of the meter. While the general utility industry move from mechanical to solid state meters has resulted in less tampering, the issue still persists. Many modem meters, such as energy and demand meters, contain sensors that can point to tampering; these are known as tamper codes. While not all meters contain these codes, where they exist, they provide valuable data to identify bypass and fraud. Monitoring the tamper codes in relation to fluctuation of consumption can be a strong identifying component to determine suspected fraud. Another major component is monitoring consumption patterns compared with consumption history at the property as well as the consumption of like residences or businesses. These comparisons help determine whether such consumption patterns are explainable.
Non-metering is another phenomenon that is difficult to detect without monitoring typical network distribution characteristics. An example could include setting up a separate circuit for a pool at a residence but not getting the line metered. Typically this is the result of contractors not getting appropriate permits and setting up illegal circuits. Since there is no measurement of the consumption, these are likely best measured by determining what circuits in the electrical distribution network have large unexplained line leakage or increases or decreases in consumption.
Another factor to consider in revenue assurance is network failure. Network failure has revenue impacts due to the inability to provide and thus bill for service when the network is down. Beyond billing concerns, network failure can also lead to customer churn as customers may leave the utility company where a failure was experienced to join another company with perceived higher reliability. In addition to the revenue impacts to utility companies through quality of service, network failure can in some instances result in public safety issues given that many households demand on reliable service to heat or cool their homes during dangerously high temperatures, or to run critical medical equipment, the interruption of which can be life-threatening.
Determining network failure is extremely important to ensure proper quality of service and personal safety. There are a variety of issues that can lead to network failure including non-metered circuits, transformer age/failure, and peak times of electrical demand. Transformers tend to overheat during periods of high-temperature increasing the probability of failure, this coupled with the direct relationship between energy consumption and temperature fluctuations across the portions of the electrical network can create an increased risk of massive failure. Detecting changes in demand can enable redistribution of utilities or equipment upgrades to prevent a network failure. In situations where meters have aged or may be undersized, prediction of likely failures can help prevent the instances and extent of network failures. The need for a utility company to predict network failure is extremely important to the stability of their business.